{"as_of":"2026-08-09T20:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b2836b531fb4aa2a8b4f77338d6d3b0dad8cdb1cd7c7d51cf9601daf35f7bd2a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":19,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T17:37:39.507833Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T19:17:17.632979Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-08-09T17:37:39.507833Z","title":"Relu2 wins: Discover- ing efficient activation functions for sparse llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00840","last_updated":"2025-06-10T17:24:15Z","snapshot_observed_at":"2026-08-09T17:29:43.365534Z","submitted_at":"2025-02-02T16:25:48Z","title":"Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T17:37:39.507833Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2502.00840"},"observation_digest":"sha256:9ba7a12e9c7ccf91a13e4e130a29ef74381fe6a3d3850524b5a957246cf50146","observation_id":"287bf1e2-4352-430b-ac76-5ab02d64ee48","resolution":{"observed_at":"2026-08-09T17:37:39.507833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-08-09T15:48:57.426567Z","title":"Relu ^2 wins: Discovering efficient activation functions for sparse llms, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01330","last_updated":"2025-08-13T09:51:20Z","snapshot_observed_at":"2026-08-09T15:37:50.694045Z","submitted_at":"2025-02-03T13:09:21Z","title":"Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-09T15:48:57.426567Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2502.01330"},"observation_digest":"sha256:e6b54e75f21c955325eb5ca42e49287fb9e5f1becbd9bcb24aa0b4c626620151","observation_id":"2f34fcf1-2e4b-4e23-b20d-f0b3a8f928a1","resolution":{"observed_at":"2026-08-09T15:48:57.426567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-08-06T22:28:08.715111Z","title":"Relu ^2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21797","last_updated":"2025-07-01T18:25:12Z","snapshot_observed_at":"2026-08-09T16:41:19.942682Z","submitted_at":"2025-06-26T22:40:30Z","title":"Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T22:28:08.715111Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2506.21797"},"observation_digest":"sha256:9da2d07335831b4e05f166c5f67ad484dc97dd032b7896ae78ab50c48e3f4346","observation_id":"40040ca2-e341-43ff-a3dc-123c505b6d54","resolution":{"observed_at":"2026-08-06T22:28:08.715111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-08-06T18:20:00.484184Z","title":"ReLU ^2 wins: Discovering efficient activation functions for sparse LLMs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08771","last_updated":"2025-07-30T04:14:15Z","snapshot_observed_at":"2026-08-07T06:32:52.508610Z","submitted_at":"2025-07-11T17:28:56Z","title":"BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-06T18:20:00.484184Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2507.08771"},"observation_digest":"sha256:eb83fad188412d06e1a8523fb93ef50957c4eb1db5232eb3ce47696533107c84","observation_id":"01cbe2a3-37ea-4fb4-8e35-e1dc8e9e3ef6","resolution":{"observed_at":"2026-08-06T18:20:00.484184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-08-06T13:09:56.533540Z","title":"Zhang, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.20984","last_updated":"2025-07-30T06:29:40Z","snapshot_observed_at":"2026-08-06T15:52:46.358709Z","submitted_at":"2025-07-28T16:45:14Z","title":"SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T13:09:56.533540Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2507.20984"},"observation_digest":"sha256:c73c676283694f30b2d22b3b59d455ead0fdf77b1e904d2e8ebb7fed360b5a3f","observation_id":"85ee922e-f1ab-4c36-b5dd-a71cce40314d","resolution":{"observed_at":"2026-08-06T13:09:56.533540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2512.12744","last_updated":"2026-05-21T01:18:21Z","snapshot_observed_at":"2026-08-07T14:28:26.092742Z","submitted_at":"2025-12-14T15:47:40Z","title":"Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-16T22:19:25.483640Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2512.12744"},"observation_digest":"sha256:a2fef5005c962fa60e67fea27a25c6f30974cb9af3040283dc83ecdb99cdc692","observation_id":"f7ba7e3f-7e6d-4472-ad5e-83c3cfdb7346","resolution":{"observed_at":"2026-05-16T22:21:18.973013Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2512.12744","last_updated":"2026-05-21T01:18:21Z","snapshot_observed_at":"2026-08-07T14:28:26.092742Z","submitted_at":"2025-12-14T15:47:40Z","title":"Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-22T11:54:29.436149Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2512.12744"},"observation_digest":"sha256:5b9845e4c8f2f238064da36d4eb410113cbbd8dec1f47fd0e9862b6fb4613f5c","observation_id":"ebd87e62-d818-459d-a6ee-8f1ba7a219b2","resolution":{"observed_at":"2026-05-22T11:54:51.166503Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-08-03T15:22:56.382733Z","title":"Relu 2 wins: Discovering efficient activation functions for sparse llms.arXiv preprint arXiv:2402.03804, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17351","last_updated":"2026-07-28T16:53:50Z","snapshot_observed_at":"2026-08-03T18:14:53.588030Z","submitted_at":"2025-12-19T08:47:28Z","title":"Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-03T15:22:56.382733Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2512.17351"},"observation_digest":"sha256:b9a48b622ae4ad7dd981b4afea7a5c958bc4c8d43486f7cbefd09c97ff1903aa","observation_id":"bcd43197-16ec-429a-aaca-503f6635dea3","resolution":{"observed_at":"2026-08-03T15:22:56.382733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2604.12946","last_updated":"2026-04-14T16:43:37Z","snapshot_observed_at":"2026-07-06T23:01:05.634599Z","submitted_at":"2026-04-14T16:43:37Z","title":"Parcae: Scaling Laws For Stable Looped Language Models","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-10T15:33:04.442462Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2604.12946"},"observation_digest":"sha256:612c22edf4ebe4c282666a3cbc4dcd106ce519f5d8116c2de268e4bb69fda3b2","observation_id":"7077b6b3-3a94-4a55-89da-f01a581e644d","resolution":{"observed_at":"2026-05-11T10:21:01.138815Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2604.24169","last_updated":"2026-04-29T07:44:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-27T08:24:55Z","title":"PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-08T04:43:54.832085Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2604.24169"},"observation_digest":"sha256:d4401f01b2f8c2544b70b84bf369f96de1ac339988b7c6cf9cf9251cb30cc95b","observation_id":"3c8c1dfc-ee14-425f-a63b-91faf47b244f","resolution":{"observed_at":"2026-05-11T21:36:18.093343Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2604.25150","last_updated":"2026-05-07T20:49:50Z","snapshot_observed_at":"2026-08-05T13:42:29.338033Z","submitted_at":"2026-04-28T02:53:27Z","title":"The Role of Symmetry in Optimizing Overparameterized Networks","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-07T16:53:30.229571Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2604.25150"},"observation_digest":"sha256:132dd30a1d7c6aa2b8b236f541e1000ca1699b12319f3f20b98cfe15b8d4b3f8","observation_id":"b9361ec4-799f-40f6-848d-6f9a5cacc4d2","resolution":{"observed_at":"2026-05-11T23:31:15.426128Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2604.25150","last_updated":"2026-05-07T20:49:50Z","snapshot_observed_at":"2026-08-05T13:42:29.338033Z","submitted_at":"2026-04-28T02:53:27Z","title":"The Role of Symmetry in Optimizing Overparameterized Networks","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-11T00:59:12.261460Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2604.25150"},"observation_digest":"sha256:2102f7e9a078a67cecd6c93db5147d532e68deb5336c896b8ba9284b8ee65da9","observation_id":"2639d1f2-48c3-448b-a1e9-6b404ae26151","resolution":{"observed_at":"2026-05-11T04:55:58.620267Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2605.17659","last_updated":"2026-05-20T20:56:41Z","snapshot_observed_at":"2026-08-03T00:42:33.895000Z","submitted_at":"2026-05-17T21:29:20Z","title":"Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-20T13:46:32.405079Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2605.17659"},"observation_digest":"sha256:26691532b14280dbcea9559ff622a987536c8e940da3a40403375a652a4f8169","observation_id":"30a0bd63-8769-4429-bb5c-4526375eb570","resolution":{"observed_at":"2026-05-20T13:48:19.644508Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2605.17659","last_updated":"2026-05-20T20:56:41Z","snapshot_observed_at":"2026-08-03T00:42:33.895000Z","submitted_at":"2026-05-17T21:29:20Z","title":"Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-22T09:15:32.395442Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2605.17659"},"observation_digest":"sha256:e501ff9e871c2a3eb94da4e535c7c6be23d251700f53ecee5195f9058e8ea0ec","observation_id":"b4ebf4f8-f677-4de6-92e1-4ceec936a723","resolution":{"observed_at":"2026-05-22T09:16:19.699136Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2605.23061","last_updated":"2026-05-21T21:50:22Z","snapshot_observed_at":"2026-08-02T04:10:09.683333Z","submitted_at":"2026-05-21T21:50:22Z","title":"Anytime Training with Schedule-Free Spectral Optimization","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-25T05:38:16.958574Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2605.23061"},"observation_digest":"sha256:022d478b164c56609a3f978a2bfe28d95a693d8855ddc72c237f089927a35270","observation_id":"b92b3a43-0820-419f-af66-e9f00abc0dc5","resolution":{"observed_at":"2026-05-25T05:40:24.331701Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2605.26632","last_updated":"2026-06-01T03:03:29Z","snapshot_observed_at":"2026-07-06T23:36:29.930024Z","submitted_at":"2026-05-26T07:09:49Z","title":"RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-06-29T19:40:42.033793Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2605.26632"},"observation_digest":"sha256:741852678d644c53603a5c7baa4b38fe3267679158dbe6ce4174e56bfc9cf3f7","observation_id":"50288ef7-acbf-4f4e-a6f6-726120bd4fb1","resolution":{"observed_at":"2026-06-29T19:43:54.728381Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2605.26647","last_updated":"2026-05-26T07:30:53Z","snapshot_observed_at":"2026-08-06T11:53:53.007749Z","submitted_at":"2026-05-26T07:30:53Z","title":"More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-29T19:37:51.563121Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2605.26647"},"observation_digest":"sha256:f860b62b858adca1a02ad1d074946b4f90e9e15a6c789fcce49b3f1e5f617464","observation_id":"f7dfc51b-69b5-4ae7-82f6-142080e4be9a","resolution":{"observed_at":"2026-06-29T19:43:54.835580Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":"2402.03804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-07-02T19:17:17.632979Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse llms","venue":null,"work_id":"313c45fb-6bb6-4766-92e0-3d4e946c5e53","year":2024},"citing_paper":{"arxiv_id":"2607.00158","last_updated":"2026-06-30T20:34:45Z","snapshot_observed_at":"2026-08-03T04:05:58.992502Z","submitted_at":"2026-06-30T20:34:45Z","title":"Readable but Not Controllable: Neuron-Level Evidence for Medical LLM Hallucination","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-02T19:16:31.055024Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2607.00158"},"observation_digest":"sha256:b96c2f4aeacf27bbb1526df53b3a76ed65642eec25a32c910217aeddabf2b0b3","observation_id":"651bf497-87b6-4403-9d40-04fe69698b12","resolution":{"observed_at":"2026-07-02T19:17:17.635430Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03804","snapshot_observed_at":"2026-08-08T04:21:30.142851Z","title":"ReLU 2 wins: Discovering efficient activation functions for sparse LLMs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02995","last_updated":"2026-08-04T01:21:29Z","snapshot_observed_at":"2026-08-09T10:31:32.184491Z","submitted_at":"2026-08-04T01:21:29Z","title":"SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channels","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T04:21:30.142851Z"},"links":{"cited_paper":"/paper/2402.03804","citing_paper":"/paper/2608.02995"},"observation_digest":"sha256:5c20182ed1bab7a94351632da35601a8c046747714bf0805c519c3072838dfb6","observation_id":"7879e60c-da60-439b-ad24-41cebf40c861","resolution":{"observed_at":"2026-08-08T04:21:30.142851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.03804/citation-record","integrity":"/paper/2402.03804/integrity","json":"/paper/2402.03804/citation-record.json","paper":"/paper/2402.03804"},"outbound":[],"paper":{"arxiv_id":"2402.03804","last_updated":"2024-02-06T08:45:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T17:32:30.666755Z","submitted_at":"2024-02-06T08:45:51Z","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2402.03804."}